Majority of overlapping spectra deconvolution approaches operate under the principle that certain prior knowledge about the overlapping component system must be implemented, hence known in advance. There are several existing approaches in achieving this. If the identity of components and hence their pure mass spectra is known in advance, one can use linear algebra protocols, such as those disclosed in U.S. Pat. No. 5,247,175 or U.S. Pat. No. 7,105,806, to find the relative abundances of each component. Similarly, if certain fragment ions are free of interferences (i.e. are not overlapping), the ion chromatograms can be converted into component-chromatograms using the branching ratios obtained from pure standards (US 2005/0165560). Among other approaches, if the peak shapes of the co-eluting components can be modeled or guessed in advance, this information can then be implemented into the deconvolution process (U.S. Pat. No. 4,353,242).
However, there is multitude of situations where none of the above prior information can be known with certainty. Hence, in systems where the actual number of overlapping compounds is unknown and with unknown present components, the above conventional deconvolution algorithms can not be applied. Component identification from such systems in information theory is referred to as the blind deconvolution or blind source separation.
Recently, blind entropy minimization has been used to reconstruct mass spectra of pure components (Zhang, 2003). The method employed by Zhang, 2003 involved the use of a stochastic search algorithm (also known as probabilistic or randomized algorithm), which while effective is too slow to tackle real-life problems.
There remains a need in the art for a deconvolution approach that is both fast and effective for extracting pure component mass spectra from unresolved or partially resolved mixtures of mass spectra.